期刊
IEEE TRANSACTIONS ON MOBILE COMPUTING
卷 21, 期 5, 页码 1892-1908出版社
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2020.3030061
关键词
Sensors; Data models; Measurement; Adversarial machine learning; Mobile computing; Security; Cooperative spectrum sensing; system security; attacks and defenses; adversarial machine learning
资金
- NSF [2029875]
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [2029875] Funding Source: National Science Foundation
This paper proposes a novel adversarial machine learning framework called Learning-Evaluation-Beating (LEB) to mislead the fusion center in cooperative spectrum sensing. The LEB attack effectively beats existing defense strategies by creating malicious sensing data using a surrogate model. Additionally, a non-invasive defense method named influence-limiting defense is introduced to reduce the overall disruption caused by LEB attack.
Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsified versions of spectrum sensing data to a fusion center. However, existing studies usually assume network or attackers as passive entities, e.g., assuming the prior knowledge of attacks is known or fixed. In practice, attackers can actively adopt arbitrary behaviors and avoid pre-assumed patterns or assumptions used by defense strategies. In this paper, we revisit this security vulnerability as an adversarial machine learning problem and propose a novel learning-empowered attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion center. Based on the black-box nature of the fusion center in cooperative spectrum sensing, our new perspective is to make the adversarial use of machine learning to construct a surrogate model of the fusion center's decision model. We propose a generic algorithm to create malicious sensing data using this surrogate model. Our real-world experiments show that the LEB attack is effective to beat a wide range of existing defense strategies with an up to 82 percent of success ratio. Given the gap between the proposed LEB attack and existing defenses, we introduce a non-invasive method named as influence-limiting defense, which can coexist with existing defenses to defend against LEB attack or other similar attacks. We show that this defense is highly effective and reduces the overall disruption ratio of LEB attack by up to 80 percent.
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